Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam-Reference-Cited by-同舟云学术

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Prediction of Flash Flood Susceptibility of Hilly Terrain Using Deep Neural Network: A Case Study of Vietnam

Published:2023 Issue:3 Volume:135 Page:2219-2241
ISSN:1526-1506
Container-title:Computer Modeling in Engineering & Sciences
language:en
Short-container-title:

Author:

Thi Thanh Ngo Huong,Duc Dam Nguyen,Thi Bui Quynh-Anh,Al-Ansari Nadhir,Costache Romulus,Ha Hang,Duy Bui Quynh,Hung Mai Sy,Prakash Indra,Thai Pham Binh

Publisher

Computers, Materials and Continua (Tech Science Press)

Subject

Computer Science Applications,Modeling and Simulation,Software

Reference54 articles.

1. Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas;Cao;Sustainability,2016

2. A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping;Bui;Catena,2019

3. Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?;Pham;Journal of Hydrology,2021

4. A review of risk perceptions and other factors that influence flood mitigation behavior;Bubeck;Risk Analysis,2012

5. An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines;Choubin;Science of the Total Environment,2019

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1. A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques;Water;2024-07-22

2. Solving flood problems with deep learning technology: Research status, strategies, and future directions;Sustainable Development;2024-06-08

3. Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications;Computer Modeling in Engineering & Sciences;2024

4. An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate;Computer Modeling in Engineering & Sciences;2024

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